Which DBMS Environment is Good to take it up Data Warehousing

Although relational databases (RDBMS) are the most common choice for data warehouse implementations, their record-based structure is far from ideal. As data volumes grow and users demand more sophisticated analytical capabilities, the deficiencies of the RDBMS to data storage become more conspicuous. RDBMS data warehouse systems are difficult to design; extremely inefficient in their use of disk space and I/O; challenging to maintain; and, worst of all, require designers to compromise between optimizing query performance and maximizing query flexibility.

In response to these shortcomings, alternative database architectures—columnar and correlation— have been developed. Columnar databases use less disk space and are more efficient in their I/O demands than records-based data warehouses but force their own compromise between optimizing for new record insertion versus data selection and retrieval.

A radically new data warehouse platform, the correlation DBMS (CDBMS), eliminates all such design tradeoffs. There is virtually no upfront design effort required.

The CDBMS builds a datadriven schema and indexes 100% of data values automatically during the data loading process, which optimizes for both performance and query flexibility. A CDBMS minimizes disk storage and I/O requirements, makes new data ready for use as soon as the load process is complete, and enables creation and execution of unique types of queries not supported by either RDBMSs or columnar databases.

We need to compare the data storage models, hardware demands, performance levels, design considerations and analytical flexibility of all three data structures in an analytics environment ​